6th European Conference - Academic Conferences

5.2 ROC curves
Kesav Kancherla and Srinivas Mukkamala
ROC is a graphical plot between the sensitivity and specificity. The ROC is used to represent the
plotting of the fraction of true positives (TP) versus the fraction of false positives (FP). The point (0, 1)
is the perfect classifier, since it classifies all positive cases and negative cases correctly. Thus an
ideal system will initiate by identifying all the positive examples and so the curve will rise to (0, 1)
immediately, having a zero rate of false positives, and then continue along to (1, 1). Detection rates
and false alarms are evaluated for steganography data set and obtained results are used to plot the
ROC curves. In each of these ROC plots, the x-axis is the false alarm rate, calculated as the
percentage of normal video frames considered as steganograms; the y-axis is the detection rate,
calculated as the percentage of steganograms detected. A data point in the upper left corner
corresponds to optimal high performance, i.e., high detection rate with low false alarm rate (Egan,
1975) Figures 4 and Figure 5 gives the ROC curves obtained during testing.
Figure 4 Gives the Receiver Operational Characteristics (ROC) curve obtained during steganalysis of
YASS at block size 9, compression rates 50-50 and coefficients used for embedding equal
to 12
Figure 5: Receiver Operational Characteristics (ROC) curve obtained during steganalysis of YASS at
block size 14, compression rates 75-75 and coefficients used for embedding equal to 10
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